Preprocess images: Difference between revisions
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=== RELION will work best if your data | === RELION will work best if your data are === | ||
* '''Clean''' from false particles (no images are discarded during refinement). | * '''Clean''' from false particles (no images are discarded during refinement). | ||
Line 12: | Line 12: | ||
* '''Normalised''' (the exact procedure does not matter too much, as errors in the normalisation are corrected internally) | * '''Normalised''' (the exact procedure does not matter too much, as errors in the normalisation are corrected internally) | ||
And then, just like with any other refinement program, you might save yourself lots of trouble if your data | And then, just like with any other refinement program, you might save yourself lots of trouble if your data have: | ||
* '''high signal-to-noise''' ratios (take great care in sample preparation and data collection) | * '''high signal-to-noise''' ratios (take great care in sample preparation and data collection) |
Revision as of 20:53, 27 September 2011
RELION will work best if your data are
- Clean from false particles (no images are discarded during refinement).
- Xmipp implements an image sorting utility called
xmipp_sort_by_statistics
that is very handy in the cleaning of a data set.
- Xmipp implements an image sorting utility called
- Unmasked (masking is performed internally)
- Non-interpolated (prevent any prior rotations/translations: use the originally scanned pixel values)
- If downscaling is necessary because of memory issues: use a window-operation in Fourier-space, not a convolution (e.g. with rectangle/B-spline).
- Xmipp implements the Fourier-space downscaling in the
xmipp_scale
program with the-fourier
option.
- Uncorrected for CTF (this is done internally)
- If your data have previously been phase-flipped, that's OK: just tell RELION about it
- If your data have previously been pre-Wiener filtered, that's a very bad thing to do in general: go back to the original data.
- Normalised (the exact procedure does not matter too much, as errors in the normalisation are corrected internally)
And then, just like with any other refinement program, you might save yourself lots of trouble if your data have:
- high signal-to-noise ratios (take great care in sample preparation and data collection)